Application of neural networks to the reconstruction of diffuse optical tomography neuroimages
Granstedt, Jason Louis
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https://hdl.handle.net/2142/124655
Description
Title
Application of neural networks to the reconstruction of diffuse optical tomography neuroimages
Author(s)
Granstedt, Jason Louis
Issue Date
2024-04-15
Director of Research (if dissertation) or Advisor (if thesis)
Anastasio, Mark A
Doctoral Committee Chair(s)
Anastasio, Mark A
Committee Member(s)
Forsyth, David A
Wang, Yuxiong
Culver, Joseph P
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Neural Networks
Deep Learning
Image Reconstruction
Diffuse Optical Tomography
Neuroimaging
Language
eng
Abstract
Neuroimaging has led to several advancements in understanding brain functionality and neurological disorders. Diffuse optical tomography (DOT) offers a safe and portable neuroimaging modality. However, the resolution of DOT lags behind functional magnetic resonance imaging (fMRI). One avenue for increasing this resolution is via more advanced reconstruction methods. Neural networks have recently achieved state-of-the-art performance for many reconstruction problems, but conventional methods require a set of paired images for training not currently available for DOT. This project proposes a method for improving the resolution of DOT images via neural network reconstruction techniques. A dataset of paired images for training networks is constructed through exploiting the correlation between DOT and fMRI neuroimages. A virtual imaging pipeline was constructed to simulate DOT measurements from fMRI images and applied to a dataset with retinotopic stimuli. An SVD and null space analysis were performed to characterize the DOT imaging system. Three state-of-the-art network architectures are modified to support the ill-conditioned low-rank DOT imaging operator and employed for the reconstruction problem. Hallucination maps and in-distribution Rayleigh tasks were developed to properly assess the quality of the network reconstructed images and compare them with conventional methods. The networks trained on simulated data were then adapted to in-vivo DOT data and compared to the current state-of-the-art reconstruction approach. Statistically significant decoding results on in-vivo DOT data were observed for several models for the considered DOT imaging systems, indicating that the developed methods are promising for future development of network-based reconstructions for DOT applications.
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